{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "主要用于直观的展示声音数据的样式，对声音数据有直观的了解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.io import wavfile\n",
    "\n",
    "sampling_freq,audio = wavfile.read(r'C:\\E\\jupyter_notebook\\voice_reco\\Dataset\\z200\\G0002\\session01\\T0055G0002S0001.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (33471,)\n",
      "Sampling Freq： 16000\n",
      "Datatype: int16\n",
      "Duration: 2.092\n"
     ]
    }
   ],
   "source": [
    "print('Shape:', audio.shape)\n",
    "print('Sampling Freq：',sampling_freq)\n",
    "print('Datatype:', audio.dtype)\n",
    "print('Duration:', round(audio.shape[0] / float(sampling_freq), 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -70, -129, -180,  -70,  -22,   61,   92,   94,   96,   69,   -7,\n",
       "         -6,  -10,  -38,  -32,  -13,  -28,   13,   29,    5,  -22,   47,\n",
       "         84,   84,   94,  105,   75,   12,  -72, -136, -168, -101,    8,\n",
       "         67,  117,   49,   -6,    1,  -66, -116, -128, -113,  -95,  -96,\n",
       "        -96, -136, -222, -263, -170, -115], dtype=int16)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "audio[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2395647d400>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(audio)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
